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The IADS Academy programme, a 30-year-old tailor-made mentoring workshop open only to our members’ high potentials, promotes cooperation and future orientation. Over the years, the IADS Academy has trained 200+ executives from 29 companies in 22 countries, some of whom reached top positions in member and non-member companies (for IADS member companies alone, 4 CEOs).

Every year since 2020, IADS member CEOs have defined the question they want the Academy cohort to work on. In 2020, the Academy group examined the COVID-19's consequences. In 2021, the topic was about the definition of an omnichannel P&L. In 2022, the cohort focused on improving the profitability of Private Labels. In 2023, the topic was about the skills of the future. In 2024, the group addressed the question of an AI decision-making tool for department stores. Finally, the 2025 topic was: How to become better merchants: from intuition to data-driven decisions.

For more than a century, the department store merchant has been defined by mastery of numbers and product: margin discipline, inventory velocity, vendor leverage. Those skills built retail empires. Today, they no longer guarantee relevance.

This IADS Exclusive outlines the insights the Academy cohort studied, considered, and developed throughout the journey to their final presentation. The Academy examined the paradox at the heart of contemporary department stores: organisations rich in data, experience and infrastructure, yet constrained by misalignment, inertia and outdated success metrics. As traditional optimisation logic collides with 21st-century uncertainty, the role of the merchant is being quietly but fundamentally rewritten. From curator of product to Experience Architects, from data accumulation to decision clarity, the next era of merchant excellence demands a redefinition of skills, scope and accountability, to learn when to trust data, when to trust intuition, and how to orchestrate both in service of the customer.

The merchant reimagined: beyond buying and selling

Caught between eras: department store legacy meets disruption

The modern merchant operates in a state of tension between traditional success metrics (margin management, inventory turns, vendor relationships) and uncertainty. Economic, geopolitical headwinds and supply chain disruptions have shifted from exceptional events to baseline assumptions. As a result, historical competencies are necessary but insufficient.

The paradox emerges most clearly in companies’ data inventories. Department stores have extensive data and analytics infrastructure, yet performance gaps persist due to conflicting strategic priorities and the absence of a clear hierarchy or integration. The constraint is not data scarcity but alignment failure. This misalignment manifests across functions. Finance teams optimise for profitability and cost containment. Buying teams prioritise assortment productivity, vendor relationships and margins. Store operations focus on traffic conversion and labour efficiency. Marketing aims to build brand awareness and drive customer acquisition. Each function interprets organisational priorities through its operational lens.

The department store model faces additional structural pressures. The concession model dominates in some markets. While it delivers breadth and margin, it can alter talent development pathways. When fewer merchants practice full P&L accountability for inventory risk, recruiting and developing buyers with accountable buy-and-sell-through capability becomes progressively more difficult. Organisations become biased toward space allocation proficiency over the buying acumen that historically defined merchant excellence.

Simultaneously, brand ecosystem volatility accelerates. The lifecycle of emerging brands with rapid spike-and-fade patterns complicates buys, allocation models and space planning. Blockbuster brands deliver scale but limited differentiation. Niche players offer uniqueness but operational complexity. Merchants must navigate this while budgets contract, P&L scrutiny intensifies, and larger periods of discounts erode full-price sales.

Beyond spreadsheets: the Experience Architect

The response to these pressures cannot be incremental optimisation. As suggested by the Academy cohort, the transformation centres on repositioning the merchant from product curator to Experience Architect, a new strategic role where selling products becomes the means, not the end, and building authentic consumer intimacy becomes the focus.

Working closer to the marketing teams, Experience Architects serve as content curators, both online and offline, and ecosystem orchestrators. They are involved in the entire consumer experience from start to finish. They use AI and other technologies to enhance team effectiveness while creating unique interactions for product discovery and purchase. They coordinate suppliers and partners to deliver exciting experiences. Using live data and insights, they adapt trends, market shifts and consumer behaviour.

Yet scope expansion carries risks. How far should the merchant scope extend beyond products to include experiences, services and cross-selling? While historic merchants like Selfridges owned responsibilities far beyond products and numbers, the Experience Architect is more of a cross-functional, enterprise role rather than a siloed function. They pilot mission-oriented agile teams focused on customer missions rather than category silos.

Decode, automate, orchestrate, delight: the new merchant skillset

The Experience Architect role requires capabilities beyond traditional merchandising competencies. Four foundational pillars can define merchant effectiveness in volatile environments:

• Decode the customer: merchants must unveil their motivations, emotions, track emerging signals, not only purchases. The shift is from analysing what customers buy to understanding why they buy.
• Automate to elevate: AI and automation can absorb routine tasks, freeing merchants to focus on strategy. From that perspective, McKinsey research suggests automation will significantly impact planning, pricing and inventory replenishment, shifting merchant focus from data collection to interpretation and action.
• Orchestrate connected offers: merchants must craft value propositions and curate assortments serving distinct missions and needs. This also extends beyond channel management to encompass omnichannel optimisation.
• Delight at speed: the capability to adopt continuous test-and-learn rhythms, move quickly on trends and create moments that surprise and build loyalty. This represents a fundamental shift from seasonal cycles to real-time responsiveness.

Moreover, modern merchants require a new set of skills: stronger data interpretation skills, strategic thinking, cross-functional collaboration across merchandising, design, marketing, and supply chain, technological proficiency, and a deeper understanding of consumer psychology.

However, the skills progression is non-linear. Early-career merchants typically rely more on data than intuition as they build foundational knowledge. Experience gradually strengthens intuition, as a “muscle strengthened by experience,” as suggested by the Academy participants. Senior merchants may operate at a 70% intuition-30% data, reflecting accumulated pattern recognition, not reduced analytical rigour. The intuition-data-driven balance also depends on company culture and data availability.

The art-science balance: when to trust data and when to trust intuition

Understanding when each approach excels

The Academy cohort discussed the intuition-versus-data debate and acknowledged that “gut feeling" and "hard numbers" are often still opposed. The usual framework for when intuition deserves trust requires a somewhat predictable environment, opportunities to learn through significant practice, and high-quality, rapid feedback. In retail merchandising, core customer behaviours (seasonal shopping patterns, category preferences, price sensitivity) show sufficient regularity for pattern recognition. However, volatile factors such as emerging brand lifecycles, social media trends, and economic disruptions introduce unpredictability. But this is not a reason to avoid considering intuition. Even analytics-obsessed organisations recognise that there is more to significant strategic decisions than data alone. For example, Google, an early adopter of big data, had the intuition that self-driving cars were possible well before data was available. Intuition also plays a significant role in Google’s Project X, the department inventing and launching “moonshot” technologies.

Ultimately, intuition excels at generating hypotheses, interpreting context and identifying weak signals that data can miss. Data excels at detecting patterns across large datasets and maintaining consistency in application.

In risk environments, alternatives, consequences and probabilities are known. Also, optimisation and statistical thinking are paramount. In uncertain environments, variables themselves are unknown. Retail increasingly operates under genuine uncertainty rather than quantifiable risk, yet the industry somehow continues to apply 20th-century optimisation logic to 21st-century uncertainty.

This suggests equipping merchants with scenario-based heuristics rather than purely algorithmic recommendations. For example: "If returns spike 10% in week one, investigate manufacturing quality." Such rules may outperform complex models by acknowledging uncertainty and enabling rapid human response, rather than waiting for sufficient data to clarify patterns.

From data-driven to decision-driven retailing

Data-driven decision-making pitfalls exist. They share a common root: treating data as self-interpreting rather than requiring thoughtful evaluation. The belief that gathering more data and feeding it to powerful algorithms alone can reveal truth and create value is a dangerous mistake. IADS Academic Advisor, Professor Robert Rooderkerk from RSM Erasmus University, reinforced this perspective during a lecture with the Academy cohort by differentiating data-driven and decision-driven approaches:

• Data-driven organisations ask "what data do we have?"
• Decision-driven organisations ask "what decisions need to be made and what data supports them?"

Retail analytics maturity shows when the question is not about existing data but about the decisions to be made and the data supporting them. Rooderkerk introduced a five-level analytics maturity model that most organisations struggle to ascend:

• Descriptive (what happened), where most organisations remain stuck.
• Diagnostic (why it happened).
• Predictive (what will happen).
• Prescriptive (how to make it happen).
• Autonomous (continuous optimisation).

In fashion merchandising, the art-science tension is definitional, not problematic. Creative instinct and aesthetic judgment remain foundational. In that environment, data provides guardrails and validation, not replacement. A healthy friction between creative push and analytical prudence drives optimal outcomes. An 80/20 balance emerges: most products should be commercially driven with data validation, while a smaller portion serves creative, aspirational brand-building that accepts lower immediate returns for long-term positioning.

The three pillars for execution: organisation, curation and experimentation

Structures that enable rather than constrain

Organisational structure powerfully shapes decision quality, yet structure alone cannot compensate for cultural dysfunction or misaligned incentives. The Academy's comparative analysis across department stores revealed structural similarity despite differences in scale and geography. Most organisations maintain 3-8 hierarchical buying levels, 1:1 buyer-to-planner ratios at operational tiers, and separation between buying (product selection, vendor relations) and planning (financial planning, allocation, inventory management).

This separation introduces inherent tension. Buyers and planners share KPIs but operate under different functional leadership, which can create conflicting priorities. Multiple hierarchical layers delay decisions. When multiple buyers handle a single brand across categories, brand message coherence suffers. Yet the structure also offers advantages: clear career progression, defined responsibilities, collaboration, and team-level business ownership.

During a brainstorming session, Doctor Christopher Knee, IADS Honorary Advisor, encouraged piloting mission-oriented agile teams composed of a buyer, marketer, analyst, and operator, focused on customer missions rather than category silos. Yet Olivier Bron, Bloomingdale’s CEO and Academy Mentor, cautioned against structure obsession. The imperative is "process over structure", fixing how plans cascade end-to-end rather than redrawing org charts. Brand-facing decisions must translate seamlessly through marketing, floor execution, staffing, training, and storytelling. Finally, mutual misunderstandings undermine execution. Stores underestimate market work intensity for buying teams, and merchants underestimate store-level constraints. Joint accountability for success and failure must span functions.

The curation imperative: MediaMarkt vs. Coolblue

Rooderkerk shared a powerful example of the choices retailers and merchants face: the contrast between MediaMarkt and Coolblue (a Dutch electronics retailer) crystallises a fundamental strategic choice facing retailers. MediaMarkt is a legacy player with 1,000+ stores, 75% of revenue from physical retail, and aggressively pursuing a marketplace model. To illustrate this strategy, they added 50,000 SKUs from third-party sellers in nine months, with some categories reaching 50% marketplace fulfilment. The strategy intentionally reduced owned inventory by eliminating low-rotation SKUs. Trade-offs emerged immediately: scale and capital efficiency versus loss of control over fulfilment and inconsistent customer experience.

Whereas MediaMarkt is betting on marketplace breadth, Coolblue, a digitally native company expanding its physical presence, curates depth. Coolblue’s curated assortment has already significantly increased store revenues. They developed a “consideration matrix,” a decision tree organised by consumer-relevant attributes (brand, price, performance, use case) rather than margin tiers. A dynamic dashboard tracks SKUs, unique SKUs sold, average price, gross margin, return rate, sales growth, customer satisfaction and market share. The result: Coolblue reduced online SKUs from 30,000 to 20,000 while increasing sales and gaining market share. Their NPS exceeds 75, rivalling Apple. This shows merchant success lies not in offering more, but in knowing precisely what not to offer and why.

This case encapsulates the Experience Architect mandate. Merchants must deeply understand customer decision processes (consideration matrix), continuously monitor comprehensive performance metrics (dynamic dashboard), make disciplined exclusion decisions that require conviction, and maintain cross-functional alignment to deliver experiences that justify a high NPS.

Building a culture of experimentation: test (and fail), learn, scale

One of the Academy findings was that alignment, not data scarcity or tool sophistication, is the primary constraint. Organisations possess customer data, but they lack consensus on how to interpret priorities and make decisions. The diagnostic exercise that the Academy cohort recommended exposes this reality:

• Document every significant organisational priority,
• Have each senior leader independently articulate the end goal for each priority,
• Share assessments to expose misalignment,
• Use findings to build alignment before deploying tools or processes.

Without this alignment, sophisticated analytics generate conflicting signals that paralyse action. Finance, buying, operations, and marketing optimise for different outcomes, each believing they serve “the customer.”

Cultural transformation proves essential yet gradual. The Academy participants embraced a "failing fast and cheap" framework: rapid, small-scale testing of concepts, in which failures generate learning without existential risk, and successes can be scaled. This requires reframing failure from career risk to learning opportunity, a shift that demands psychological safety.

Booking.com and Netflix exemplify this approach through systematic, collaborative, codified experimentation cultures. A/B testing controls for self-selection. Experiments are jointly designed by the business and analytics teams. Results are recorded in searchable repositories and iterated based on cumulative insights. This is decision-driven analytics: experiments test specific hypotheses about customer behaviour rather than exploring data in search of patterns.

The path forward: turning merchant philosophy into operational reality

Beyond traditional metrics: rewriting KPIs to reward relationships over transactions

The Academy cohort identified a fundamental KPI shift, going from “what the customer does for the organisation” (sales, margin, conversion) to “what the organisation does for the customer,” Customer Lifetime Value (CLV). This is not semantic repositioning, as it requires new performance reward systems, broader metrics to include creativity and impact alongside financial results, and the integration of customer-focused KPIs across the organisation.

CLV naturally lengthens time horizons. Merchants managing quarterly sales targets would probably make different decisions than those optimising lifetime customer relationships. CLV rewards differentiation over discounting, service quality over transaction speed, and brand experiences over commodity fulfilment. It aligns merchandising incentives with marketing (brand building), operations (service, delivery) and finance (sustainable profitability).

However, CLV implementation faces obstacles. Calculating robust CLV requires integrated data across online and offline channels, attribution models that handle omnichannel journeys, cohort analysis that segments customers, and patience to accumulate sufficient data before models stabilise.

The transitional approach uses dual metrics: maintain traditional transaction KPIs (essential for immediate accountability) while building CLV measurement capability and progressively increasing its weight in merchant evaluations, then in company-wide evaluations. This parallels the art-science balance: traditional metrics provide guardrails and CLV metrics guide strategic direction.

A merchant’s playbook: where data decides and where intuition leads

During the course of the 9-month programme, Academy participants developed an 8-stage merchant product lifecycle framework mapping where science should dominate, where intuition should lead and where integration should create value:

1. Pre-market remains science-driven: analysing past performance, financial guardrails and SKU frameworks that prevent undisciplined buying.
2. Go-to-market requires a shift from transactional negotiation to intuition-driven curation. Balancing financial constraints with customer-centric instincts and emotional connection. This is where merchant taste matters.
3. Post-market order writing: best practice merges with pre-market analysis for integrated planning.
4. Post-buy operations remain science-driven: inventory flow optimisation, allocation algorithms, vendor coordination, logistics efficiency.
5. Product education, where lies an opportunity for change from the usual approach (fact-heavy technical data) to a renewed approach explaining emotional connection and why customers should buy: storytelling that contextualises the product within customer aspirations.
6. Visual directives: this part should also change from a historical approach, where consistency and uniformity prevail, to a new approach: storytelling vehicles, immersive narratives, cross-category storytelling that creates experiences rather than product displays.
7. In-season maintenance: reducing reactive dashboard monitoring to real-time trend chasing, bold bets, and experimental inventory management. Data enables rapid response, and intuition determines what trends merit amplification.
8. End-of-season reset remains science-based (margin management, clearance optimisation, inventory disposition) but informed by richer in-season insights.

This lifecycle clarifies where to invest in analytical automation (stages 1, 4, 8) versus human judgment enhancement (stages 2, 5, 6, 7). It prevents the dual errors of over-automating creative stages and under-automating operational stages.

AI that empowers merchants’ intuition

So far, AI cannot replace human intuition and imagination. These capabilities are part of the Experience Architect mandate. The Academy's final positioning on AI is pragmatic: AI handles routine analytical workflows, liberates merchants for "art" (storytelling, partnership building, curation, intelligent risk), accelerates brand scouting and assortment optimisation while keeping final judgments human.

Knee offered clarification: AI represents high-speed calculation, not intelligence. Value is handling repetitive tasks, freeing merchants for creative/strategic work. The danger lies in the “it wasn't me, it was the technology” abdication, where AI recommendations serve as accountability shields. Knee recommends building AI sandboxes for safe experimentation: controlled environments where merchants test algorithmic recommendations against their judgment, calibrate confidence based on results, and develop intuition about when to override.

However, imagination and intuition are often underdeveloped and impulsive. To elevate their decision-making processes, organisations should codify and foster the necessary human decision-making skills:

• Rejecting simplistic dataism: effective decision-making means integrating AI in a more human-led process, not only relying on data analysis and algorithmic optimisation.
• Ensuring decision-makers “get their hands dirty” with direct engagement with stakeholders.
• Making implicit skills like intuition explicit through reflection and training. Experiential learning questions can be: what was my first reaction, where did I rely on individual or collective experience, where did I supplement my experience, what mental shortcuts did I rely on to simplify the decision?
• Fostering psychological safety where diverse perspectives can thrive,
• And finally, building hybrid systems that combine human and AI strengths.

The Academy's transformation from "how do we become better merchants?" to "how do we become Experience Architects who use data to inform intuition and intuition to interpret data" represents a maturation from capability focus to purpose focus.

When participants asked CEOs, "How do you want your customers to feel?" during the Academy final presentation, the collective answer was: inspired, valued and cherished. Achieving this requires transcending transactions. Discovery must feel personal. Every visit should spark emotion. Customers must feel part of a community. The merchant's purpose is to put customers at the heart of every decision, with their experience guiding organisational purpose.

This is not soft aspiration disconnected from commercial reality. It is the integration that makes commercial success sustainable. Data tells merchants what customers did. Intuition helps explain why they did it and what they might value next; merchants often need to know what customers want before they do.

The department stores that will thrive are those that resolve the paradox not by choosing between intuition and data, but by building organisational architectures in which both inform every decision. The merchants who will succeed are those who match this sophistication not by replacing their judgment with algorithms, but by cultivating the capability to let data inform intuition and intuition guide which data matters. That integration, commercially grounded, customer-obsessed, and continuously evolving, defines merchant excellence in the age of both big data and irreplaceable human insight.

Credits: IADS (Christine Montard)